DNA Other Cells/ Organisms Mutaon Mang€¦ · Other Cells/ Organisms Other Environmental Factors Cell‐cell signals Change genome Change environment Produce Remediate Sequester

Ma#ng

Muta#on

DNAMigra#on

ACell/Organism

OtherCells/

Organisms

OtherEnvironmental

Factors

Cell‐cellsignals

Changegenome Changeenvironment

Produce

Remediate

Sequester

Support

Cure

Ecosystem/Tissue

Measure/manipulatehere

GoalsandOutcomes

Scien#ficGoals

Techno

logies

ImprovedAnnota#onHigh‐throughputPhysiology/Gene#cs

Frommacromolecularstructure

NonlinearOp#miza#onMul#variateSensi#vityAnalysis

Sta#s#calGraphModelingAlgorithmsSemi‐automatedmodelgenera#onScalablemethodsforhybrid/mul#scalesim.

Bifurca#onanalysisforlarge/mul#physicssys.IntegratedsoMwaresystemsExperimentalDesignSupport

ExascaleResources

CloudCompu#ng

TransparentresourceuseRapidDetermina#onofGenomicPoten#al

Environmentalchange,growth,andevolu#on

Remediate,sequester,produce,cure

Automatedmodelreduc#on(abstrac#on)

GrandChallenges

•  ModelingPhenotypefromGenotypeAcrosstheTreeofLife.–  Rapidreconstruc#onofcellularnetworksacrossmul#ple#meandspace

scales.•  Predic#vemodelsof10,000organisms•  Mechanis#cunderstandingofsinglecelldynamics

–  Predictenvironmental“inputs”andoutputs:“crackthesignalingcode”*–  Predictop#malgrowthcondi#onsfromgenotype–  Op#mizenaturalsystemac#vityofindividualsandconsor#a–  Individualizedgenomics

•  DiscoverandEngineerUnitsofFunc#onatallNetworkScales–  Discovertheevolu#onaryprinciplesoffunc#ongenera#onandreuse–  Designnewfunc#onfromcomposi#onofsuchmodules

*Thanks,Mark

ExampleStories:ChrisHenry(andCostasMaranas)

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Scien:ficobjec:ve:Developa“complete”understandingofthemetabolismofB.sub'lisincludingiden#fica#onofalltransporters,theregula#onofallmetabolicpathways,andthepathwayresponsetotheenvironment.

Whatswitchesonspores?

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Impact

•  Wehaveanopportunitytotrulyunderstandawholelifeformanditsinterac#onwithitsenvironment.

•  Wecanplaceitinevolu#onarycontextandbegintounderstandhowitanditspartsarosefromoriginallyinorganiccomponents?

•  Wecandiscoverthemo#fsoffunc#onthatwecanexploitforhumanpurposes

OverallConclusions•  Dataiss#llthemainbo^leneckbuttherearebadlyscalingcomputa#onalproblemsintheir

analysis

•  Behaviorisresponsivetocomplexcombina#onsofinputsandcombinatoriallyaffectedbylargenumbersofgenesthusexplora#onofmodelsortes#ngagainstdataremainsdifficult.

•  Forthisefforttohaveaprofoundimpactonbiologyweneedhighthroughput,easyuse,andfastconcisepresenta'onofdata,model,andcomparison.

•  Whilethereisfundamentaltheoryandalgorithmsthatneedtobedeveloped–andiscri'cal–thetransparentaccesstolargecomputa#onresourcesforalargenumberofusersisthekey.Mosttoolswillbeembarrassinglyparallelorclosebutwills#llrequireoutrageousnumbersofprocessorsforeachproject.Andtherewillbemanyusers.

•  Sadly–experimentsseemtoscalealongsidecomputa#on–thelargerandmorecomplexthemodelthemoreexperimentsareneededtotest.–  Thescalingmaynothavenearthesameexponentsbutitisfarfromtrivialin#meandcost–  Thedatais“fragile”andneedstobe#ghtlyqualitycontrolled,instantlyaccessible,andwell

annotated

–  Computa#onshouldbeaneverydayadjutanttoexperiment–butthecomputa#onalthroughputiscurrentlytooslowandcomplicatedtobeasgenerallyusefulasaknockoutormicroarray.

Annota#on

•  Sequencingisscalingsuchthat10k’sofgenomesandevenmoremetagenomicsequencewillbesequenced/unit#me

•  Newfamiliesareplateauing,buts#ll30‐50%unannotatedfunc#on.Needstructurepredic#onhelp.Experimentsarekey.–  Investments

•  Newalgorithmsforphylogene#cannota#onN^2Log(N)–  Deeptheoryofevolu#onfortrees.

•  Newalgorithmsforstructuralannota#on(Seemacromoleculargroup)•  Newalgorithmsfor“guilt‐by‐associa#on”(M*N^2)•  Experimentstobroadenfunc#onalassignments(NOrganism*nCond*Nreplic)

•  Manualannota#on/cura#on

High‐throughputPhysiology/Gene#cs

•  Problemsofscale–  Fromsinglemoleculetopopula#onsofcells–  Mul#plexesmachinesallowsomewholegenomeassays–butbiochemistryiss#llaproblem,imagingiss#lla

problem(~N^3)andtechlikemassspecitselfcomputa#onalproblemsinspectramatching(~N^2)–  Gene#csiss#llcase‐by‐case–  Qualityandreproducibility–  Centraliza#on/dissemina#onofdataandstandards

•  Investments–  Determiningmethodsforqualitycontrolwithdatacollec#onwithtechnologieshighdistributedinindividual

laboratories(ratherthanacentralresource).–  Investmentincrea#nghigherthroughput/highcontentresourcesforbiochemistry,singlecellandsingle

molecule(andinsitu)imaging/measurement–  Investmentsinnewculturingtechnologiesinmicrofluidicstobioreactors–  Massivelyparallelcomputa#onMUSTbematchedbymassivelyparallelexperiment.–  Increasingcomputa#onintheloopdesignofexperiments

•  Mathema#csandCSforDesignofexperiments–  Transparentintegra#onofsupercompu#ngandtheHTexperimentlabs.

•  Embeddedsystemsengineeringforplacingcontrolsontheinstruments–  Visualiza#onofcomplexdatasetsderivedfromthese.–  Mathema#csforreduc#onofnonlinearmul#variatedatasets.–  Imageprocessing/segmenta#on‐–  Capturereagents(totargetmoleculesformeasurement)–  Experimentsthatseektodiscoverthecommunica#onmechanismsandmoleculesbetweencells.

Sta#s#calGraphModelingAlgorithms

•  Theoryfordeterminingbestarrayofexperimentstoinputintothemodel–  Whichmodali#es–  Whichcondi#onsandtheircombina#ons–  Which#mescales–  Howmanyreplicates

•  Currently–biclustergoesasN^2/N^3andpropaga#onanduncertainandbranch/boundmodelreduc#ongoesasExp(cNedges)wherecsmallconstant.

•  Investments–  Newtheoryfordataintegra#onandsta#s#calmodeling–  Validatedtestsetsforalgorithmcomparison–  Mechanismsfordataavailability–  Detec#onweaksignalsinhighbackgroundnoise(notsubgraphisomorphism)–

sequences,correla#ons,quan#fica#ons–  Networkmo#fdetec#on–  Automatedtextmining/naturallanguageprocessing–  Improvethefeedbackloopbetweensta#s#calassocia#onsandannota#on

•  Be^eruseofmolecularinterac#ondataandsequence/TFinforma#on

Semi‐automatedmodelgenera#on•  DataDriventoDynamicModels

–  Howdoweusebioinforma#canalysisofnewdatasetstobuildamodel?–  Howdoweforce“annota#on”ofmorebiochemicalfeaturesthatwouldaidmodelers.–  Metabolicreconstruc#onthroughannota#on

•  Valida#on•  Feedbackthroughinconsistencyandholes

–  Rapidupdateofmodelswhendataupdates•  Linktopapers

•  Ataxonomyofmodeltypesthatwouldallowyoutraversewhatkindofmodeltogeneratefromdata.

•  Establishmentofstandardsfordifferenttypesofmodel–  Whatisa“complete”model?

•  Howdoyouintegratemodelsofdifferentpartsofalargersystem–  Toolsformakingthiseasier.–  Controlledvocab,seman#csandagreementwiththebioinforma#csfolkswithnaming

•  We’dliketo–wehavenoclue?•  MaybeaccessibilitytotheSta#s#calmodels

scalablemethodsforhybrid/mul#scalesim.

•  Tounderstandhowthe1Dgenomeistransformedinto3Dspace.•  CurrentPDEsimula#onsscaleasO(N)but…•  Needforusablelibraryandmodelingframeworksthatbiologistscanuseeasily.•  Dothiswithhighheterogeneityinthephysics(stochas#c/determinis#c)andspace.•  Dothisunderuncertaintyindata,parametersand(gasp)mechanism•  Languagesformodelrepresenta#ons:Formallanguages?(Languagesforsimula#oncontrol).

•  Investments–  Howdosmoothlytransi#onamonglevelsofphysicalabstrac#onfromfullyrenderedspa#al/molecularlevel

simula#on,throughmesoscalestochas#csallwaytosmoothedODE.–  Howdoyoucomparespa#al/temporaldataandmodelsinphylogene#ccontext.–  Howdoyouputtogethergene#c/genomicdataandspa#almodeling.–  Howdoyoubuildcomplexheterogeneousmodels?–  Howdoyoubuildhybridstochas#c/determinis#cmodels/sta#s#cal–  Newintegrators(likeMagnusExpansions,spectral)tosupportaccurate–  Measurementtechnologyofbiochemicalac#vity(andforces,etc.)inlivecells.

Automatedmodelreduc#on

•  Formalcoarsegraining,scalesepara#on,and“balancedtrunca#on”– RonaldCoifman?

•  Automatednondimensionaliza#on?•  Linearalgebraicsolu#onsfortheabove.– Pseudospectralmethods?– Regulariza#on

•  Responsesurfacemethodsandfunc#onalapproxima#on.

NonlinearOp#miza#on

•  Parameteres#ma#onandModelTes#ng–  Determina#onoftheerrorboundsonthemodel–  Parameterfeasibilityregions

•  Changesinregionsuponcomposi#ngofsubmodels–  Theoryofmoduleimpendanceandretroac#vity

–  ModelInvalida#on•  Inconsistency

–  Datainvalida#on–  Algorithmsforglobalop#miza#onbycleverparametermo#on

•  Quasirandomsearch–  Howdowegettothetailsofthedistribu#onsofour“stochas#c”models.

•  Measuresofparameterconserva#onbetweenorganismsasaresultandproxyforevolu#onarypressure.

•  ModelSelec#on/ModelModifica#on–minimalnumberofmovestoexplainthemaximumamountofdata.(Exp(N)butprac#callyN^2toN^3).

•  Mul#objec#veop#miza#on•  IntegerOp#miza#on

Mul#variateSensi#vityAnalysis

•  Differencemethodsfailforstochas#csystems–  Newalgorithm–  Petzold&Doyle?

•  Highdimensionalsensi#vity–  Sethnaandsloppiness.

•  Eigendirec#ons.–  DavidRand(Warwick)–  FAST(Fouriersomethingorsomething)Saltelli–  DeniseKirchner

•  Understandingwheretodoyourexperiments•  Howdowelinksensi#vitytoevolvability?

Bifurca#onanalysisforlarge/mul#physicssys.

•  Auto,Oscill8,MatLab(CONTENT)–  Crashesaround25differen#alequa#ons

•  Howdoesbifurca#onchangewithcomposedmodels?–  Highdimensional(co‐dim3andhigher)

•  Classifica#onofdynamicsofamodel–  Rapidinferenceofpossibilityforbifurca#onfrommodels.–  ChemicalReac#onNetworkTheory(Feinberg)–  Differentphysicalmodelclasses.

•  Fullydiscreteandstochas#cmethods–  Dodeterminis#cbifurca#onssurviverealis#cnoise–  Donewbifurca#onsariseduenoise.

•  Mapparameterstogenotypedeterminewhichgenotypesleadtobifurca#on.

ExperimentalDesignSupport

•  Op#mizechoiceofdatatoanswerques#onbasedonmodel–  E.g.bestdatatoes#mateparameters–  E.g.bestdatatodiscriminatebetweenmodels–  E.g.bestdatatodiscovermissingpiecesofmodel–  E.g.bestdatatoreducetheuncertaintyinmodelpredic#on.

–  Tidor–  Arkin/Flaherty

•  Theautomated“Adam”–thegene#cist.•  Issuesofresolu#on

–  Timeresolu#on–  Rela#vevs.absolutemeasurement–  Measuresofconstraint?–  Whatdatadoyouneedtodealwithmul#scalemodels

•  ClosedloopcontrolofBiology–  ThinkHerschelRabitz

Italldependsonwhatthemeaningof“Mission”is.

Yes,wecan!